research-article

Natural language queries over heterogeneous linked data graphs: a distributional-compositional semantics approach

Authors:

Andre Freitas,

Edward CurryAuthors Info & Claims

IUI '14: Proceedings of the 19th international conference on Intelligent User Interfaces

Pages 279 - 288

https://doi.org/10.1145/2557500.2557534

Published: 24 February 2014 Publication History

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Abstract

The demand to access large amounts of heterogeneous structured data is emerging as a trend for many users and applications. However, the effort involved in querying heterogeneous and distributed third-party databases can create major barriers for data consumers. At the core of this problem is the semantic gap between the way users express their information needs and the representation of the data. This work aims to provide a natural language interface and an associated semantic index to support an increased level of vocabulary independency for queries over Linked Data/Semantic Web datasets, using a distributional-compositional semantics approach. Distributional semantics focuses on the automatic construction of a semantic model based on the statistical distribution of co-occurring words in large-scale texts. The proposed query model targets the following features: (i) a principled semantic approximation approach with low adaptation effort (independent from manually created resources such as ontologies, thesauri or dictionaries), (ii) comprehensive semantic matching supported by the inclusion of large volumes of distributional (unstructured) commonsense knowledge into the semantic approximation process and (iii) expressive natural language queries. The approach is evaluated using natural language queries on an open domain dataset and achieved avg. recall=0.81, mean avg. precision=0.62 and mean reciprocal rank=0.49.

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Cited By

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Butkienė RŠukys AAblonskis LButleris R(2024)Influence of Event Specialization Strategy on Some Aspects of Natural Language Querying Interfaces to OntologiesIEEE Access10.1109/ACCESS.2024.348988912(165780-165796)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3489889
Chen YLu ELin Y(2022)Efficient SPARQL Queries Generator for Question Answering SystemsIEEE Access10.1109/ACCESS.2022.320679410(99850-99860)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3206794
Chen YLu EOu T(2021)Intelligent SPARQL Query Generation for Natural Language Processing SystemsIEEE Access10.1109/ACCESS.2021.31306679(158638-158650)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3130667
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Index Terms

Natural language queries over heterogeneous linked data graphs: a distributional-compositional semantics approach
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Data management systems
  2. Information retrieval
    1. Document representation

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Reviews

Reviewer: Yingjie Li

Using natural language to query large amounts of heterogeneous linked data is a difficult problem. This paper proposes a distributional-compositional semantics approach to answer queries over linked data. Without heavily relying on ontologies, this approach is data centered and driven. Researchers in natural language processing (NLP), linked data, the semantic web, and query answering will want to study this work. The proposed approach first constructs a distributional semantic vector space, including a term vector space and a concept vector space, for a given data collection. The term vector space is built upon all terms available in the given dataset. The concept vector space is built upon the extraction of co-occurrence patterns of each term from the dataset. Then the resource description framework (RDF) graph data is rewritten using the term and concept vector spaces. Each RDF predicate is represented to be a weighted concept vector, and each RDF instance is represented to be a weighted term vector. Based on the distributional and compositional semantic vector space, and given a natural language query, the proposed system first extracts a set of query features and resolves the query into a RDF-like format. After the query is analyzed, the system generates a query processing plan that maps the extracted query features and the semi-structured query representation into a set of search, navigation, and transformation operations over the queried data graph. Finally, the operations of the query processing plan are executed over the semantic vector space to identify the results to the query. The experiments tested the proposed approach using the question answering over linked data 2011 test collection (QALD-1). The results demonstrate that the proposed system achieves better recall, precision, and reciprocal rank compared to other systems, including PowerAqua and FREyA. However, the paper would have been more comprehensive if the authors had evaluated the proposed system over more than one dataset. Online Computing Reviews Service

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Information & Contributors

Information

Published In

IUI '14: Proceedings of the 19th international conference on Intelligent User Interfaces

February 2014

386 pages

ISBN:9781450321846

DOI:10.1145/2557500

General Chairs:
Tsvi Kuflik
The University of Haifa, Israel
,
Oliviero Stock
FBK-IRST, Italy
,
Program Chairs:
Joyce Chai
Michigan State University
,
Antonio Krüger
Saarland University and DFKI GmbH, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2014

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IUI'14

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IUI'14: IUI'14 19th International Conference on Intelligent User Interfaces

February 24 - 27, 2014

Haifa, Israel

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IUI '14 Paper Acceptance Rate 46 of 191 submissions, 24%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Butkienė RŠukys AAblonskis LButleris R(2024)Influence of Event Specialization Strategy on Some Aspects of Natural Language Querying Interfaces to OntologiesIEEE Access10.1109/ACCESS.2024.348988912(165780-165796)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3489889
Chen YLu ELin Y(2022)Efficient SPARQL Queries Generator for Question Answering SystemsIEEE Access10.1109/ACCESS.2022.320679410(99850-99860)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3206794
Chen YLu EOu T(2021)Intelligent SPARQL Query Generation for Natural Language Processing SystemsIEEE Access10.1109/ACCESS.2021.31306679(158638-158650)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3130667
De Santo AHolzer ABernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Interacting with Linked Data: A Survey from the SIGCHI PerspectiveExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3334480.3382909(1-12)Online publication date: 25-Apr-2020
https://dl.acm.org/doi/10.1145/3334480.3382909
Polus MAbbas T(2020)Intelligent Text Clustering Based on Semantics Similarity2020 1st. Information Technology To Enhance e-learning and Other Application (IT-ELA10.1109/IT-ELA50150.2020.9253127(60-66)Online publication date: 12-Jul-2020
https://doi.org/10.1109/IT-ELA50150.2020.9253127
Vishwakarma SDhole A(2019)A Novel IR for Relational Database using Optimize Query BuildingInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT195438(251-257)Online publication date: 5-Aug-2019
https://doi.org/10.32628/CSEIT195438
Al-Smadi MAl-Dalabih IJararweh YJuola P(2019)Leveraging Linked Open Data to Automatically Answer Arabic QuestionsIEEE Access10.1109/ACCESS.2019.29562337(177122-177136)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2956233
Hu XDuan JDang D(2019)Crowdsourcing-based semantic relation recognition for natural language questions over RDF dataEnterprise Information Systems10.1080/17517575.2019.159738513:7-8(935-958)Online publication date: 30-Mar-2019
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Curry EDerguech WHasan SKouroupetroglou Cul Hassan U(2019)A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart EnvironmentsFuture Generation Computer Systems10.1016/j.future.2018.07.01990(405-422)Online publication date: Jan-2019
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Khan URafi M(2018)Semantic Oriented Document Clustering Using Distribution SemanticsProceedings of the 2nd International Conference on Information System and Data Mining10.1145/3206098.3206110(14-18)Online publication date: 9-Apr-2018
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A distributional approach for terminological semantic search on the Linked Data Web

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